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Paper detail

Correcting Knowledge Base Assertions

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB.

preprint2020arXivOpen access
Jiaoyan ChenXi ChenIan HorrocksErnesto Jimenez-RuizErik B. Myklebus
Artificial Intelligence
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Jiaoyan ChenXi ChenIan HorrocksErnesto Jimenez-RuizErik B. Myklebus

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